Every global organization is on a journey with artificial intelligence (AI). As leaders and teams move from being “AI literate” to being “AI fluent,” conversations are no longer just about innovation but about trust, risk and governance.

The World Economic Forum’s Global Risks Report 2026 identifies adverse outcomes of AI technologies as the risk with the largest rise in perceived severity over time. This risk has moved from number 30 in the two-year outlook to number five in the 10-year outlook. That long-term warning is already showing up as a near-term business challenge.

As AI becomes more deeply embedded in reporting, forecasting and enterprise decision-making, the implication is clear: trusted data is one of the strongest defences organizations have against AI risk.

Workiva’s 2026 Executive Benchmark Survey found that more than half of business leaders say data problems, led by lack of real-time data (29%) and limited access to siloed data (28%), are impeding strategic impact.

This problem is particularly significant for non-financial and sustainability data, which too often remain disconnected from the financial data that shape enterprise reporting and decision making.

Research from NYU Stern’s Center for Sustainable Business, through its Return on Sustainability Investment (ROSI) methodology, reinforces the point from a value-creation perspective: organizations often generate real business value from sustainability-related action but fail to track and use that information effectively for collective decision-making.

How organizations translate sustainability data into business value

Real-world examples show how organizations are translating their sustainability metrics into measurable business outcomes.

For example, Reformation, a global women’s clothing retailer, found that its take-back, resale and recycling programmes generated financial value through reduced input costs, increased earned media and customer acquisition benefits, resulting in a $1.9 million net financial benefit.

Energy and emissions data also offer clear operating insights. Working with Gundersen Health Services, CSB found that energy retrofits could deliver savings of approximately $1 per square foot, while new net-zero construction could achieve savings of $2 per square foot.

With healthcare responsible for roughly 10% of greenhouse gas emissions from commercial buildings in the United States, the opportunity for both cost reduction and emissions improvement is significant.

When organizations connect sustainability efforts to financial outcomes, they not only see value earlier but also build the connected data foundation AI needs.

How trusted AI depends on trusted data

Connected data is trusted data. When financial, operational and non-financial data come together in a governed environment, organizations can better trace inputs and validate outputs. They can also spot inconsistencies earlier and better understand how performance and risk interact across the business.

The companies best positioned to lead in this AI-powered economy will be those that can trust the data behind it, not those that deploy AI the fastest. NIST’s AI Risk Management Framework further outlines the characteristics of trustworthy AI systems.

These include being valid and reliable, accountable and transparent, and fair, with harmful bias managed. This shifts the conversation away from AI as a standalone capability and toward the strength of the data environment in which it operates. Many organizations, however, are still trying to scale AI on top of fragmented data environments.

In addition to limiting strategic impact, Workiva’s survey found that poor data quality contributes to bad or delayed operational decisions, followed by regulatory fines or scrutiny and lost credibility with investors or lenders. This increases the risk of acting quickly on weak inputs and misplaced confidence.

What is the trust dividend?

Connected data can help organizations avoid bad outcomes but more than that, it creates better conditions for faster, clearer and more defensible decisions. When leaders can work from a shared, governed view of financial, operational and sustainability data, they can better validate what AI is surfacing, connect non-financial data to enterprise performance and act with greater confidence.

That is the dividend: not just cleaner reporting but stronger judgment. Workiva’s survey directly supports this point, with 96% of respondents saying that better access to shared data increases the likelihood of achieving optimal business outcomes. This is especially important for sustainability-related decisions.

While sustainability reporting requirements continue to evolve globally, investor and stakeholder expectations for decision-useful, connected data remain firm.

At NYU Stern, we lean on the ROSI methodology, which measures the financial returns on sustainability activities and bridges the gap between sustainability strategies and financial performance.

That makes ROSI useful here not as a separate framework article but as an example of the trust dividend in practice: business value is easier to see, measure and act on when relevant data is connected rather than scattered across functions.

Why C-level alliance is key for better data

As previously reported by the Forum, the C-suite must align strategy, technology and capital to get AI right for business. The same is true for operationalizing AI.

The chief sustainability officer (CSO), chief information officer (CIO) and chief financial officer (CFO) each hold a different view of the same risk, across non-financial and resilience data, systems and governance, and capital allocation decisions.

To reduce AI risk and capture the trust dividend, those perspectives are continuing to converge. Workiva’s survey shows that this shift is already taking shape:

These numbers overwhelmingly validate a broader market reality: investors want a more connected view of enterprise value, one that reflects both financial performance and the non-financial factors shaping resilience, reputation and long-term risk. No single function can meet that demand alone.

The CFO brings capital discipline and decision accountability. The CIO brings architecture, governance and implementation rigour. The CSO brings visibility into long-term risk, resilience and non-financial drivers of value. Decision-ready AI depends on all three working from the same foundation.

How organization can begin building better AI for sustainability reporting

To begin making better use of AI and drive better insights in sustainability and value creation: